近年來，車輛俯瞰監視(top-view around monitor)系統已經成為一個標準配備的駕駛輔助工具，藉此減少因盲點造成的碰撞危險。許多此類系統提供車輛周圍的環場影像，適用於擁擠空間的行駛與倒車停車。美國國家公路交通安全管理局已經發佈了規定，2018年5月後所有低於一萬英磅的新車輛都必須搭載車後影像設備。車後相機已經成為了未來的標準備配。在本論文中，我們提出了一個倒車導引與障礙物偵測系統，只需要一部車後廣角相機無須搭載其他感應器，便可在車後影像，產生俯瞰影像及倒車導引線，並標記障礙物以提醒駕駛人。
在倒車導引的部分，我們先偵測影像中的特徵點，透過絕對誤差和數值（SAD）來匹配前後時刻影像中的特徵點。每對成功匹配的特徵點都可得出對應的運動向量，再使用最小平方誤差估計法估計車輛的運動參數。基於阿克曼轉向幾何模型，車輛的運動參數可估計出車輛的運動軌跡，並根據車輛運動軌跡繪製停車導引線。
在障礙物偵測部分，透過已經得到的連續影像及估算出的車體自我運動向量，系統便可以比對偵測到特徵點與地面的運動是否相似，藉此初步篩選出不是躺平在地面的物件為候選障礙物，再透過表面法線估計評估各物件與地面法線的夾角，來確認物件為高於地面的立體物或是平躺於地面不會造成威脅的標線或圖案。以上方法在大多數情況都可以有效偵測障礙物，但偶而會受到相機震動或影像雜訊干擾。倒車導引和障礙物偵測是一個連續的動作，若不良影像只是少數幾張，則可以利用機率來排除。當系統判斷該點可能為障礙物便將該點與鄰近點增加計數，將計數累積成熱度圖後，機率高的部分視為障礙物，減少因為輸入瑕疵造成的誤判。最後根據駕駛者的喜好可於俯瞰影像或是原始影像中標記偵測到的障礙物，以提醒駕駛者潛在的危險。

In recent years, top-view monitoring systems are becoming a practical driving aid that help reducing collision hazards by eliminating blind spots. The U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) issued a rule requiring rear visibility technology in all new vehicles under 10,000 pounds by May 2018. Many of such systems provide short range views surrounding the vehicle, limiting its application to parking and reversing. In this paper, we propose a practical system for creating top-view image of the vehicle with the parking guidance line, and highlighting obstacles only relies on a wide-angle camera to capture images for analysis without sensors.
In the proposed parking guidance system, feature points on two consecutive images are extracted to match each other. First, the feature-point pairs are further pruned by Sum of Absolute Differences (SAD)。The remained pairs are then used to estimate vehicle motion parameters by a least-square error metrics, where an isometric transformation model based on the Ackermann steering geometry is proposed to describe the vehicle motion. At last, the vehicle trajectory is estimated based on the vehicle motion parameters and the parking guidance lines are drawn according to the vehicle trajectory.
In the proposed obstacle detection system, by estimating the ego-motion of the vehicle using the input image sequence of the cameras, the system is able to detect objects in the images by finding movements of features that do not correspond to ground motion relative to vehicle motion. Then confirm it by the surface normal estimation for the angle of object and ground. Parking guidance and obstacle detection are a continuous action, excluding error by probabilities. Increase the count in heat map when the object is detected, the object with high counts is marked as an obstacles, which are highlighted in the multi-view imagery to warn the driver of potential hazards.

[1] Agrawal, M., K. Konolige, and M. R. Blas, "CenSurE: Center surround extremas for realtime feature detection and matching," in Proc. of 10th European Conf. on Computer Vision, Marseille, France, Oct.12-18, 2008, pp.102-115.
[2] Alpine, "Alpine HCE-C500 Topview camera system", http://www.alpine-europe.com/p/Products/camera58/hce-c500
[3] Ansari, M., S. Mousset, and A. Bensrhair, "Temporal consistent real-time stereo for intelligent vehicles," Pattern Recognition Letters, vol.31, no.11, pp.1226-1238, 2010.
[4] Armingol, J., A. Escalera, C. Hilario, J. M. Collado, J. P. Carrasco, M. J. Flores, J. M. Pastor, and F. J. Rodríguez, "IVVI: Intelligent vehicle based on visual information," Robotics and Autonomous Systems, vol.55, no.12, pp.904-916, 2007.
[5] Barron, J. L., D. J. Fleet, and S. S. Beauchemin, "Performance of optical flow techniques," Int. Journal of Computer Vision, vol.12, no.1, pp.43-77, Feb. 1994.
[6] Bay, H., A. Ess, T. Tuytelaars, L. Gool, "SURF: Speeded up robust features", Computer Vision and Image Understanding (CVIU), vol.110, No.3, pp.346–359, 2008.
[7] Bertozzi, M. and A. Broggi, "GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection," IEEE Trans. on Image Processing, vol.7, no.1, pp.62-81, Jan. 1998.
[8] Bertozzi, M., A. Broggi, P. Medici, and P. P. Porta, "Stereo vision-based start-inhibit for heavy goods vehicles," in Proc. of IEEE Intelligent Vehicles Symp., Tokyo, Japan, June 13-15, 2006, pp.350-355.
[9] Bishop, R., eds., Intelligent Vehicle Technology and Trends, Artech House, Boston, Massachusetts, 2005.
[10] Braillon,C., C. Pradalier, J. Crowley, C. Laugier, L. Gravir, and I. Rhone-alpes, "Real-time moving obstacle detection using optical flow models," in Proc. Intelligent Vehicles Symp., Tokyo, Japan, Jun.13-15, 2006, pp.466-471.
[11] Chen, C.-M., Traffic Sign and Signal Detection and Recognition with Color Learning, Master Thesis, Department of Computer Science and Information Engineering, National Central University, Chungli, Taiwan, 2011.
[12] Chu, J., L. Ji, L. Guo, Libibing, and R. Wang, "Study on method of detecting preceding vehicle based on monocular camera," in Proc. IEEE Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004, pp.750-755.
[13] Devernay, F. and O. Faugeras, "Straight lines have to be straight," Machine Vision and Applications, vol.13, no.1, pp.14-24, Aug. 2001.
[14] Ehlgen, T. and T. Pajdla, "Monitoring surrounding areas of truck-trailer combinations," in Proc. of 5th Int. Conf. on Computer Vision Systems, Bielefeld, Germany, Mar.21-24, 2007, pp.207-218.
[15] Ehlgen, T., M. Thom, and M. Glaser, "Omnidirectional cameras as backing-up aid," in Proc. of IEEE 11th Int. Conf. on Computer Vision, Rio de Janeiro, Brazil, Oct.14-21, 2007, pp.1-5.
[16] Enkelmann, W., "Obstacle detection by evaluation of optical flow fields from image sequences," in Proc. of 1st European Conf. on Computer Vision, Antibes, France, Apr.23-27, 1990, pp.134-138.
[17] Fujitsu, 360-degree Wrap-around Video Imaging Technology, on http://www.fujitsu.com/us/news/pr/fma_20101019-02.html.
[18] Gandhi, T. and M. Trivedi, "Parametric ego-motion estimation for vehicle surround analysis using an omnidirectional camera," Machine Vision and Applications, vol.16, no.2, pp.85-95, Feb. 2005.
[19] Gandhi, T. and M. M. Trivedi, "Vehicle surround capture: survey of techniques and a novel omni-video-based approach for dynamic panoramic surround maps," IEEE Trans. on Intelligent Transport Systems, vol.7, no.3, pp.293-308, Sep. 2006.
[20] George, S. K., N.H.C. Yung, and G.K.H. Pang, "Vehicle shape approximation from motion for visual traffic surveillance," in Proc. IEEE Conf. Intelligent Transportation Systems, Oakland, CA, Aug.25-29, 2001, pp.610-615.
[21] Guizar-Sicairos, M., S. T. Thurman, and J. R. Fienup, "Efficient subpixel image registration algorithms," Optics Letters, Vol. 33, no. 2, pp.156-158, Jan. 2008
[22] Harris, C. and M. Stephens, "A combined corner and edge detector," in Proc. 4th Alvey Vision Conf., Manchester, UK, Aug.30-Sep.2, 1988, pp.147-152.
[23] Ho, C.-L., H.-L. Shen, H.-Y. Wang, and D.-C. Tseng, "A surrounding bird-view monitoring system with image refinement for parking assistance," in Proc. 22th IPPR Conf. on Computer Vision, Graphics and Image Processing, Shitou, Taiwan, Aug.23-25, 2009, pp.91-102.
[24] Hoiem, D., A. A. Efros, and M. Hebert, "Putting objects in perspective," Int. Journal of Computer Vision, vol.80, no.1, pp.3-15, Oct. 2008.
[25] Honda, Multi-view Camera System, on http://world.honda.com/news/2008/4080918Multi-View-Camera-System.
[26] King-Hele, D., "Erasmus Darwin's improved design for steering carriages and cars," Notes and Records of the Royal Society of London, vol.56, no.1, pp.41-62, Jan. 2002.
[27] Li, S. and Y. Hai, "Easy [tion of a blind-spot-free fisheye camera system using a scene of a parking space," IEEE Trans. Intelligent Transportation Systems, vol.12, no.1, pp.232-242, Mar. 2011.
[28] Lin, W., K. Panusopone, D. M. Baylon, M. Sun, Z. Chen, and H. Li, "A Fast Sub-Pixel Motion Estimation Algorithm for H.264/AVC Video Coding," IEEE Trans. Circuits and Systems for Video Technology, vol. 21, no. 2, pp. 237-242, Feb. 2011.
[29] Liu, Y.-C., K.-Y. Lin, and Y.-S. Chen, "Bird's-eye view vision system for vehicle surrounding monitoring," in Proc. of the 2nd Int. Conf. on Robot Vision, Auckland, New Zealand, Feb.18-20, 2008, pp.207-218.
[30] Lucas, B. D. and T. Kanade, "An iterative image registration technique with an application to stereo vision," in Proc. Int. Joint Conf. on Artificial Intelligence, Vancouver, Canada, Aug.24-28, 1981, vol.2, pp.674-679.
[31] Lowe, D. G., "Distinctive image features from scale-invariant keypoints," Int. Journal of Computer Vision, vol.60, no.2, pp.91-110, Nov. 2004.
[32] Milanés, V., D. F. Llorca, J. Villagrá, J. Pérez, C. Fernández, I. Parra, C. González, and M. A. Sotelo, "Intelligent automatic overtaking system using vision for vehicle detection," Expert Systems with Applications, vol.39, no.3, pp.3362-3373, Feb. 2012.
[33] Nissan, Around View Monitor, on http://www.nissan-global.com/EN/TECHNOLOGY/OVERVIEW/avm.html.
[34] Rosten, E. and T. Drummond, "Machine learning for high-speed corner detection," in Proc. 9th European Conf. on Computer Vision, Graz, Austria, May7-13, 2006, vol.1, pp.430-443.
[35] Saxena, A., S. H. Chung, and A. Y. Ng, "3-D depth reconstruction from a single still image," Int. Journal of Computer Vision, vol.76, no.1, pp.53-69, Jan. 2008.
[36] Shi, J. and C. Tomasi, "Good features to track," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, Jun.21-23, 1994, pp.593-600.
[37] Shyr, B., Daytime Detection of Leading and Neighboring Vehicles on Highway: A Major Capability for the Driver Assistant Vision System, Master Thesis, Electrical Engineering, National Chung Cheng Univ., Chia-yi, Taiwan, 2003.
[38] Sivaraman, S. and M. M. Trivedi, "Combining monocular and stereo-vision for real-time vehicle ranging and tracking on multilane highways," in Proc. IEEE Conf. on Intelligent Transportation Systems, Washington DC, Oct.5-7, 2011, pp.1249-1254.
[39] Surgailis, T., A. Valinevicius, V. Markevicius, D. Navikas, and D. Andriukaitis, "Avoiding forward car collision using stereo vision system," Elektronika ir Elektrotechnika, vol.18, no.8, pp.37-40, May 2012.
[40] Tsai, R. Y., "A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses," IEEE Journal of Robotics and Automation, vol.3, no.4, pp.323-344, Aug. 1987.
[41] Ueminami, K., K. Hayase, and E. Sato, "Multi-around monitor system," Mitsubishi Motors Technical Review, no.19, pp.55-58, 2007.
[42] Ullah, I., F. Ullah, Q. Ullah, and S. Shin, "Sensor-based robotic model for vehicle accident avoidance," Journal of Computational Intelligence and Electronic Systems, vol.1, no.1, pp.57-62, Jun. 2012.
[43] Yamaguchi, K., "Vehicle ego-motion estimation and moving object detection using a monocular camera," in Proc. 18th Int. Conf. on Pattern Recognition, Hong Kong, China, Aug.22-24, 2006, pp.610-613.
[44] Yang, C., H. Hongo, and S. Tanimoto, "A new approach for in-vehicle camera obstacle detection by ground movement compensation," in Proc. 11th IEEE Int. Conf. on Intelligent Transportation System, Beijing, China, Oct.12-15, 2008, pp.151-156.
[45] Yu, M. and G. Ma, "360° surround view system with parking guidance," SAE Int. Journal of Commercial Vehicles, vol.7, no.1, pp.19-24, Aug. 2014.
[46] Weng, J., P. Cohen, and M. Herniou, "Camera calibration with distortion models and accuracy evaluation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.14, no.10, pp.965-980, Oct. 1992.
[47] Wikipedia, "Speed limits by country", http://en.wikipedia.org/wiki/Speed_limits_by_country
[48] Wilkinson, L. and M. Friendly, "The History of the Cluster Heat Map," The American Statistician, vol.63, no.2, pp.179-184, Jan. 2012
[49] Zhang, Z., "A flexible new technique for camera calibration," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, no.11, pp.1330-1334, Nov. 2000.
[50] Zheng, Y., S. Lin, C. Kambhamettu, J. Yu, and S. B. Kang, "Single-image vignetting correction," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.31, pp.2243-2255, Dec. 2009.